Author: Aleksandr Korotkov, ODS Slack krotix

Tutorial

Yet another ensemble learning helper

Ensemble learning - this is a method use multiple learning algorithms to obtain(I'd say usually it so, but not in any cases) better predictive performance than could be obtained from any of the constituent learning algorithms alone.

The most common techniques are:

boosting

bagging

stacking

We are looking at some meta-algorithms to make an ensemble, which can improve the performance of a metric, get a better speed of experimenting and simplify code.

se=SequentialEnsemble(random_state=seed,shuffle=True)# The initial layer is a blended layer, same as a layer in the BlendEnsemblese.add('blend',[clf1,clf2])# The second layer is a stacked layer, same as a layer of the SuperLearnerse.add('stack',[clf1,clf3])# The meta estimator is added as in any other ensemblese.add_meta(SVC())compare(se)

Summary

We looked at different algorithms and different libraries, which might save a lot of time when you need to use ensemble technic. Ensemble modeling is a powerful way to improve the performance of your machine learning models. If you wish to be on the top of the leaderboard in any machine learning competition or want to improve models you are working on – ensemble is the way to go.